Brier score for multi-class classification problems with \(r\) labels defined as $$ \frac{1}{n} \sum_{i=1}^n \sum_{j=1}^r (I_{ij} - p_{ij})^2. $$ \(I_{ij}\) is 1 if observation \(i\) has true label \(j\), and 0 otherwise.
Note that there also is the more common definition of the Brier score for binary
classification problems in bbrier().
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
mlr_measures$get("mbrier")
msr("mbrier")
Type: "classif"
Range: \([0, 2]\)
Minimize: TRUE
Required prediction: prob
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures) for a complete table of all (also dynamically created) Measure implementations.
Other classification measures:
mlr_measures_classif.acc,
mlr_measures_classif.auc,
mlr_measures_classif.bacc,
mlr_measures_classif.bbrier,
mlr_measures_classif.ce,
mlr_measures_classif.costs,
mlr_measures_classif.dor,
mlr_measures_classif.fbeta,
mlr_measures_classif.fdr,
mlr_measures_classif.fnr,
mlr_measures_classif.fn,
mlr_measures_classif.fomr,
mlr_measures_classif.fpr,
mlr_measures_classif.fp,
mlr_measures_classif.logloss,
mlr_measures_classif.mcc,
mlr_measures_classif.npv,
mlr_measures_classif.ppv,
mlr_measures_classif.precision,
mlr_measures_classif.recall,
mlr_measures_classif.sensitivity,
mlr_measures_classif.specificity,
mlr_measures_classif.tnr,
mlr_measures_classif.tn,
mlr_measures_classif.tpr,
mlr_measures_classif.tp
Other multiclass classification measures:
mlr_measures_classif.acc,
mlr_measures_classif.bacc,
mlr_measures_classif.ce,
mlr_measures_classif.costs,
mlr_measures_classif.logloss